A genetic algorithm for community detection in complex networks

A new genetic algorithm for community detection in complex networks was proposed. It adopts matrix encoding that enables traditional crossover between individuals. Initial populations are generated using nodes similarity, which enhances the diversity of initial individuals while retaining an acceptable level of accuracy, and improves the efficiency of optimal solution search. Individual crossover is based on the quality of individuals' genes; all nodes unassigned to any community are grouped into a new community, while ambiguously placed nodes are assigned to the community to which most of their neighbors belong. Individual mutation, which splits a gene into two new genes or randomly fuses it into other genes, is non-uniform. The simplicity and effectiveness of the algorithm are revealed in experimental tests using artificial random networks and real networks. The accuracy of the algorithm is superior to that of some classic algorithms, and is comparable to that of some recent high-precision algorithms.

GLOBAL OPTIMIZATION OF PUMP CONFIGURATION PROBLEM USING EXTENDED CROWDING GENETIC ALGORITHM

An extended crowding genetic algorithm (ECGA) is introduced for solvingoptimal pump configuration problem, which was presented by T. Westerlund in 1994. This problem hasbeen found to be non-convex, and the objective function contained several local optima and globaloptimality could not be ensured by all the traditional MINLP optimization method. The concepts ofspecies conserving and composite encoding are introduced to crowding genetic algorithm (CGA) formaintain the diversity of population more effectively and coping with the continuous and/or discretevariables in MINLP problem. The solution of three-levels pump configuration got from DICOPT++software (OA algorithm) is also given. By comparing with the solutions obtained from DICOPT++, ECPmethod, and MIN-MIN method, the ECGA algorithm proved to be very effective in finding the globaloptimal solution of multi-levels pump configuration via using the problem-specific information.

An Improved Genetic Algorithm for Solving the Mixed⁃Flow Job⁃Shop Scheduling Problem with Combined Processing Constraints

The flexible job-shop scheduling problem(FJSP)with combined processing constraints is a common scheduling problem in mixed-flow production lines.However,traditional methods for classic FJSP cannot be directly applied.Targeting this problem,the process state model of a mixed-flow production line is analyzed.On this basis,a mathematical model of a mixed-flow job-shop scheduling problem with combined processing constraints is established based on the traditional FJSP.Then,an improved genetic algorithm with multi-segment encoding,crossover,and mutation is proposed for the mixed-flow production line problem.Finally,the proposed algorithm is applied to the production workshop of missile structural components at an aerospace institute to verify its feasibility and effectiveness.

Scheduling in a Meta Search Engine by Genetic Algorithm

The meta search engines provide service to the users by dispensing the users' requests to the existing search engines. The existing search engines selected by meta search engine determine the searching quality. Because the performance of the existing search engines and the users' requests are changed dynamically, it is not favorable for the fixed search engines to optimize the holistic performance of the meta search engine. This paper applies the genetic algorithm (GA) to realize the scheduling strategy of agent manager in our meta search engine, GSE(general search engine), which can simulate the evolution process of living things more lively and more efficiently. By using GA, the combination of search engines can be optimized and hence the holistic performance of GSE can be improved dramatically.

Neural network fault diagnosis method optimization with rough set and genetic algorithms

Aiming at the disadvantages of BP model in artificial neural networks applied to intelligent fault diagnosis, neural network fault diagnosis optimization method with rough sets and genetic algorithms are presented. The neural network nodes of the input layer can be calculated and simplified through rough sets theory; The neural network nodes of the middle layer are designed through genetic algorithms training; the neural network bottom-up weights and bias are obtained finally through the combination of genetic algorithms and BP algorithms. The analysis in this paper illustrates that the optimization method can improve the performance of the neural network fault diagnosis method greatly.

Application of a Genetic Algorithm Based on the Immunity for Flow Shop under Uncertainty

The uncertain duration of each job in each machine in flow shop problem was regarded as an independent random variable and was described by mathematical expectation.And then,an immune based partheno-genetic algorithm was proposed by making use of concepts and principles introduced from immune system and genetic system in nature.In this method,processing se- quence of products could be expressed by the character encoding and each antibody represents a feasible schedule.Affinity was used to measure the matching degree between antibody and antigen.Then several antibodies producing operators,such as swopping,mov- ing,inverting,etc,were worked out.This algorithm was combined with evolution function of the genetic algorithm and density mechanism in organisms immune system.Promotion and inhibition of antibodies were realized by expected propagation ratio of an- tibodies,and in this way,premature convergence was improved.The simulation proved that this algorithm is effective.

Parallel Distributed CFAR Detection Optimization Based on Genetic Algorithm with Interval Encoding

Aiming at parallel distributed constant false alarm rate （CFAR） detection employing K/N fusion rule,an optimization algorithm based on the genetic algorithm with interval encoding is proposed. N-1 local probabilities of false alarm are selected as optimization variables. And the encoding intervals for local false alarm probabilities are sequentially designed by the person-by-person optimization technique according to the constraints. By turning constrained optimization to unconstrained optimization,the problem of increasing iteration times due to the punishment technique frequently adopted in the genetic algorithm is thus overcome. Then this optimization scheme is applied to spacebased synthetic aperture radar （SAR） multi-angle collaborative detection,in which the nominal factor for each local detector is determined. The scheme is verified with simulations of cases including two,three and four independent SAR systems. Besides,detection performances with varying K and N are compared and analyzed.

Dissemination and Genetic Structure of Carbapenemase Encoding Genes (bla_OXA-23and bla_OXA-24) in <i>Acinetobacter baumannii</i>from Southern Texas

Acinetobacter baumannii is one of the most important human pathogens causing a variety of nosocomial infections. Carbapenem antibiotics have been primarily used to treat the A. baumannii infections. However, carbapenem resistant A. baumannii producing carbapenemases causes serious treatment problems worldwide. Outbreaks of carbapenem resistant isolates have reported in some area of the United States, but their dissemination and genetic structure of the carbapenemase encoding genes are currently little known. To understand outbreaks, dissemination, and genetic structure of the carbapenemase encoding genes in Southern Texas, 32 clinical isolates collected from Austin and Houston, TX were characterized. Twenty-eight of 32 isolates were resistant to all tested β-lactam antibiotics including carbapenem (imipenem and meropenem). Three of them carried blaOXA-23 as a part of Tn2008 integrated into a known plasmid (pACICU2) and all others carried blaOXA-24 flanked by XerC/XerD-like recombinase binding sites that were adjoined by DNA sequences originated from multiple plasmids. Genotype analysis revealed that the 25 isolates carrying blaOXA-24 were all identical genotypes same as a representative isolate carrying blaOXA-24 from Chicago, IL but the 3 isolates carrying blaOXA-23 was a distinct genotype as compared with isolates carrying blaOXA-23 from Chicago, IL and Washington, D.C. Each of the blaOXA-23 and blaOXA-24 was transferred to carbapenem susceptible A. baumannii and E. coli with similar minimal inhibitory concentration (MIC) of carbapenem as that of their parental isolates but significantly lower levels of MIC in E. coli. Overall results suggest that a unique strain carrying blaOXA-23 and a similar strain carrying blaOXA-24 as seen in other geographic areas are currently disseminated in Southern Texas.

Improved Real-Coded Genetic Algorithm Solution for Unit Commitment Problem Considering Energy Saving and Emission Reduction Demands

Unit commitment(UC), as a typical optimization problem in electric power system, faces new challenges as energy saving and emission reduction get more and more important in the way to a more environmentally friendly society. To meet these challenges, we propose a UC model considering energy saving and emission reduction. By using real-number coding method, swap-window and hill-climbing operators, we present an improved real-coded genetic algorithm(IRGA) for UC. Compared with other algorithms approach to the proposed UC problem, the IRGA solution shows an improvement in effectiveness and computational time.

基于组合遗传算法的电力科技创新成果多agent集成仿真

电力科技成果普遍交叉,为了降低集成电力科技创新成果耗时与能量损耗,研究基于组合遗传算法的电力科技创新成果多agent集成仿真。将底层数据库中电力科技创新资源经多种接口调配至关联库、综合评价以及专家评价三大agent模块,通过结合遗传算法与蚁群算法优点的组合遗传算法,优化解决多agent集成任务分配问题,实现电力科技创新成果高效集成。实验表明,该算法的寻优能力高、求解速度快;可规范化、结构化地集成电力科技创新成果信息;具有耗时少、能量损耗低优势。为科技创新和成果转化提供新思路。

Permutation Encoding for Pilot Coordination in Multi-user Massive MIMO

Pilot plays an essential role in a duplex communication system.Several methods have been proposed for pilot assignment over specific scenarios.With the help of permutation encoding,we implemented a genetic algorithm for optimizing pilot assignment in a multi-user massive multiple input multiple output(MIMO)system.Results show improvement on existing results especially in the case of strong user estimation rates.

Determining Cotton Fiber Gene Function via Structuring of Mutant Populations for High-Throughput Reverse Genetics

The NSF Cotton Genome Centers EST projecthas released】36000 cotton fiber EST sequencesfrom Gossypium arboreum,an A-genome diploidspecies.Of the approximately 10000 genesexpressed in rapidly elongating cotton fibers,50% or more encode unknown gene functions.The next challenge facing cotton researchers isdetermining the function of the fiber genes,andwhat role each plays in determiningagronomically important fiber traits.

Electromagnetic Treatment of Genetic Diseases

The paper offers an overview of quantum and macro gravity, two of the three pillars of the Grand Unified Theory (GUT), the other thermodynamics, developed in a series of papers since the solution of the gravitational n-body problem in 1997 (J. Nonlinear Analysis, A-Series: Theory, Methods and Applications, Vol. 30, No. 8, 1997, pp. 5021 - 5032) and consolidated in the paper, The Grand Unified Theory (J. Nonlinear Analysis, A-Series: Theory: Method and Applications, Vol. 69, No. 3, 2008, pp. 823 - 831). GUT is further advanced by the paper, The Mathematics of GUT (J. Nonlinear Analysis, A-Series: Theory: Method and Applications, Vol. 71, 2009, pp. e420 - e431) and the discovery of more natural laws in the course of analyzing and explaining the disastrous final flight of the Columbia Space Shuttle in 2004 (J. Nonlinear Studies, Vol. 14, No. 3, 2007, pp. 241 - 260). Qualitative modeling was the key to the development of GUT and its theoretical and practical applications. The relevant natural laws of GUT that provide the foundations of the Unified Theory of Evolution are stated. GUT provides the basis for the development of the electromagnetic engine and the Unified Theory of Evolution, its theoretical application, for the development of appropriate technology for electromagnetic treatment of genetic diseases such as cancer, systemic lupos erythematosus, diabetes, muscular dystrophy and mental disorder, the central focus of this paper.

一种航空装备实时系统任务快速调度方法

针对航空装备实时系统对多线程下实时任务快速调度的困难,提出了一种基于多层编码遗传算法并利用粒子群算法进行参数优化的实时系统任务快速调度方法。通过引入任务贡献矩阵,建立了以总消耗时间少和贡献值大的任务尽早完成为目标的染色体适应度值评价模型,避免重要任务的丢失。采用粒子群算法对多层编码遗传算法参数进行优化,避免陷入局部最优解,增加了收敛速度。最后对调度方法进行仿真验证,仿真结果表明,本文提出的实时系统任务快速调度模型与方法可以优化任务的执行时间,保障任务的顺利完成。通过对粒子群算法优化前和优化后的调度情况的对比,证明了优化后的模型的调度性能指标要优于参数优化前的模型,调度速度和调度效果明显提升。

面向供应链分销的多维空间Pareto边界自动谈判模型研究

随着电子商务的快速发展,自动谈判逐渐成为提升供应链系统效率的一种手段。为了优化多方参与的供应链分销谈判应用,本文将多边多属性谈判问题转化为多目标优化模型,采用改进的非支配遗传算法NSGA-Ⅲ计算多维空间的Pareto边界;然后,设计多线程谈判模型,将参与多方谈判的买卖各方拆解为多个双边谈判线程,分别在多维Pareto边界上进行谈判;进而,采用动态时间依赖策略(DTD),使Agent根据对方报价在Pareto边界上动态调整让步策略,快速达成协议。为验证模型的有效性,本文进行了大量模拟自动谈判实验。实验结果表明,所提出的改进算法和谈判流程优于领域最新研究成果,能有效提升多边多属性谈判效率,有助于多方达成共赢局面。

基于代理模型和NSGA-Ⅱ的超高强钢电阻点焊工艺参数多目标优化

为寻求超高强钢电阻点焊时最佳的焊接工艺参数,开展正交试验法设计三因素五水平的平板搭接点焊试验,以焊接时间、焊接电流和电极压力为可调的工艺参数,将熔核直径、压痕深度、拉剪强度及飞溅情况作为焊接接头质量评价指标.基于高斯过程回归和BP神经网络建立起焊接工艺参数与焊接接头质量评价指标之间关系的代理模型,训练的结果显示模型精度很高.最后利用带精英策略的非支配排序的遗传算法NSGA-Ⅱ实现多目标优化,得到各评价指标之间的最优pareto解集.经验证,各评价模型的相对误差值都很小.结果表明,该优化方法有较好的预测效果和稳定性.通过使用较少的试验数据,建立优化模型的方法对电阻点焊及其它焊接领域最佳焊接工艺参数的选取具有重要的指导价值.

基于多Agent的多任务协作时间调度算法研究

为了合理安排多任务合作中的时间,设计了基于多Agent通信的多任务协作时间调度算法,实现了额外代价最小化和窗口时间内完成任务最大化。其中,算法的求解过程利用了0-1 背包问题的最优值和最优解;对0-1背包问题的求解利用了改进的编码和进化的遗传算法,提高了运算的准确性。多任务协作时间调度算法的应用,有效地最小化了系统的额外代价,实现了多Agent系统的优化。

用于函数优化的正交Multi-Agent遗传算法

将Multi Agent系统、遗传算法和正交试验设计方法相结合,提出了一种混合进化算法———正交Multi Agent遗传算法。它以Multi Agent系统为基础,通过Agent间的相互作用与每个Agent所具有的知识和自学习功能来提高算法的全局优化能力和收敛速度;同时利用正交试验设计方法产生较好的初始种群和设计正交交叉算子以获得更好的后代;针对正交试验设计产生初始化种群在函数维数很高时需很大存贮空间的缺点,提出了子空间分割法来产生所需的初始化种群,它只需要原来存贮空间的十分之一。首先,对维数为30或100的12个标准测试函数进行仿真试验,结果表明正交Multi Agent遗传算法具有很强的全局优化能力和较快的收敛速度;其次,算法对这些标准测试函数进行高维优化(高达200维),实验结果表明正交Multi Agent遗传算法具有较好的高维搜索能力。

实施遗传算法的多Agent系统的构建与实现

针对常规遗传算法的不足,该文引入智能体技术实施遗传算法,所构建的多Agent系统能从进化环境中获取表征目前进化状态的有用信息,动态地调整进化参数,监控并调度进化操作,以期快速高效地搜索到全局最优,从而提升GA的优化性能。多个实例试验表明该算法能改善常规遗传算法的欠缺,显示出超越常规遗传算法的优良性能。

面向电子商务的基本遗传算法的Agent谈判模型

探讨Agent技术和遗传算法在电子商务网上谈判中的应用,为网上谈判的开展提供定量和优化模型.首先对三种基于Agent的主流谈判模型进行比较研究;其次,从三个角度对谈判模型进行分类,并确定基于Agent技术的谈判系统的目标和特征;进一步研究基于遗传算法的Agent谈判模型,并对模型进行仿真实验,以证明模型的有效性.该模型的特点是Agent能在谈判过程中学习和发展新的谈判策略.